For reference, we consider two typical benchmark problems: q/g identification and top-quark tagging, using the same datasets as in Refs. [1]}, [2]}.
The q/g dataset, described in Ref.[1]} is comprised of \(Z(\nu \nu ) + q\)  events (signal) and \(Z(\nu \nu )+g\)  events (background).
The events are simulated using Pythia8[4]}, which performs parton showering and hadronization.
No detector simulation is performed for this sample.
Particles (excluding neutrinos) are clustered using the anti-\(k_\mathrm {T}\)  algorithm[5]} with radius parameter \(R=0.4\) .
The 4-momenta and particle ID of particles within the leading jet are saved for jets with \(p_\mathrm {T}\in [500, 550]\)  GeV and \(|\eta |<2\) .
The dataset is split into 1.2 million training events and 400k each of validation and test events.
In our experiments, we truncate events with greater than 68 particles by discarding those with the lowest \(p_\mathrm {T}\) .
